القائمة
Reporting pipelines for CSV/JSON/Markdown exports with timestamped outputs, summaries, and post-processing.
يُحدَّد Prompt الذي يراجع المصدر أولًا بشكل افتراضي. يمكنك التبديل إلى أمر مباشر أو تنزيل نسخة محلية.
اقرأ SKILL.md وأي ملفات مرافقة يعرضها SkillsMP قبل أن تقرر التثبيت.
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
يتجاوز الأمر المباشر Prompt المخصّص للمراجعة. افحص المصدر قبل تشغيله.
npx skills add https://github.com/bobmatnyc/claude-mpm-skills --skill reporting-pipelinesيبقى الأمر في سطر واحد. مرّر أفقيًا لمراجعته كاملًا قبل النسخ.
تفضّل نسخة محلية؟ نزّل الملفات المتاحة حاليًا لدى SkillsMP.
LinkedIn automation via the Linked API CLI - fetch profiles, search people and companies, send messages, manage connections, create posts, react, comment, and run Sales Navigator and custom workflows. Use when the user wants to interact with LinkedIn.
Xquik X data automation API - Use REST or MCP for tweet search, user lookup, follower exports, media downloads, monitors, webhooks, giveaway draws, and confirmation-gated X actions.
MCP (Model Context Protocol) - Build AI-native servers with tools, resources, and prompts. TypeScript/Python SDKs for Claude Desktop integration.
استنادا إلى تصنيف SOC المهني
عرض SKILL.md
| name | reporting-pipelines |
| description | Reporting pipelines for CSV/JSON/Markdown exports with timestamped outputs, summaries, and post-processing. |
| user-invocable | false |
| disable-model-invocation | true |
| version | 1.0.0 |
| category | universal |
| author | Claude MPM Team |
| license | MIT |
| progressive_disclosure | {"entry_point":{"summary":"Generate CSV/JSON/markdown reports with timestamped filenames and summary outputs.","when_to_use":"Building reporting flows, exporting analytics results, or standardizing CSV/JSON/markdown outputs across projects.","quick_start":"1. Run the CLI that produces base data 2. Export CSV/JSON/markdown with timestamps 3. Save to reports/"}} |
| tags | ["reporting","csv","json","markdown","analytics"] |
Your reporting pattern is consistent across repos: run a CLI or script that emits structured data, then export CSV/JSON/markdown reports with timestamped filenames into reports/ or tests/results/.
# Basic run
gitflow-analytics -c config.yaml --weeks 8 --output ./reports
# Explicit analyze + CSV
gitflow-analytics analyze -c config.yaml --weeks 12 --output ./reports --generate-csv
Outputs include CSV + markdown narrative reports with date suffixes.
edgar/scripts/create_csv_reports.py reads a JSON results file and emits:
executive_compensation_<timestamp>.csvtop_25_executives_<timestamp>.csvcompany_summary_<timestamp>.csvThis script uses pandas for sorting and percentile calculations.
reports/ or tests/results/YYYYMMDD or YYYYMMDD_HHMMSS suffixesreports/ or tests/results/)narrative_report_, comprehensive_export_)universal/data/sec-edgar-pipelinetoolchains/universal/infrastructure/github-actions